Data Science Projects with Python

Download Data Science Projects with Python PDF Online Free

Author :
Publisher : Packt Publishing Ltd
ISBN 13 : 183855260X
Total Pages : 374 pages
Book Rating : 4.8/5 (385 download)

DOWNLOAD NOW!


Book Synopsis Data Science Projects with Python by : Stephen Klosterman

Download or read book Data Science Projects with Python written by Stephen Klosterman and published by Packt Publishing Ltd. This book was released on 2019-04-30 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key FeaturesTackle data science problems by identifying the problem to be solvedIllustrate patterns in data using appropriate visualizationsImplement suitable machine learning algorithms to gain insights from dataBook Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter notebook running PythonUse Matplotlib to create data visualizationsFit machine learning models using scikit-learnUse lasso and ridge regression to regularize your modelsCompare performance between models to find the best outcomesUse k-fold cross-validation to select model hyperparametersWho this book is for If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful.

Executing Data Quality Projects

Download Executing Data Quality Projects PDF Online Free

Author :
Publisher : Academic Press
ISBN 13 : 0128180161
Total Pages : 378 pages
Book Rating : 4.1/5 (281 download)

DOWNLOAD NOW!


Book Synopsis Executing Data Quality Projects by : Danette McGilvray

Download or read book Executing Data Quality Projects written by Danette McGilvray and published by Academic Press. This book was released on 2021-05-27 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online

Dear Data

Download Dear Data PDF Online Free

Author :
Publisher : Chronicle Books
ISBN 13 : 1616895462
Total Pages : 304 pages
Book Rating : 4.6/5 (168 download)

DOWNLOAD NOW!


Book Synopsis Dear Data by : Giorgia Lupi

Download or read book Dear Data written by Giorgia Lupi and published by Chronicle Books. This book was released on 2016-09-13 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates "the infinitesimal, incomplete, imperfect, yet exquisitely human details of life," in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere.

Managing Your Data Science Projects

Download Managing Your Data Science Projects PDF Online Free

Author :
Publisher : Apress
ISBN 13 : 1484249070
Total Pages : 146 pages
Book Rating : 4.4/5 (842 download)

DOWNLOAD NOW!


Book Synopsis Managing Your Data Science Projects by : Robert de Graaf

Download or read book Managing Your Data Science Projects written by Robert de Graaf and published by Apress. This book was released on 2019-06-07 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: At first glance, the skills required to work in the data science field appear to be self-explanatory. Do not be fooled. Impactful data science demands an interdisciplinary knowledge of business philosophy, project management, salesmanship, presentation, and more. In Managing Your Data Science Projects, author Robert de Graaf explores important concepts that are frequently overlooked in much of the instructional literature that is available to data scientists new to the field. If your completed models are to be used and maintained most effectively, you must be able to present and sell them within your organization in a compelling way. The value of data science within an organization cannot be overstated. Thus, it is vital that strategies and communication between teams are dexterously managed. Three main ways that data science strategy is used in a company is to research its customers, assess risk analytics, and log operational measurements. These all require different managerial instincts, backgrounds, and experiences, and de Graaf cogently breaks down the unique reasons behind each. They must align seamlessly to eventually be adopted as dynamic models. Data science is a relatively new discipline, and as such, internal processes for it are not as well-developed within an operational business as others. With Managing Your Data Science Projects, you will learn how to create products that solve important problems for your customers and ensure that the initial success is sustained throughout the product’s intended life. Your users will trust you and your models, and most importantly, you will be a more well-rounded and effectual data scientist throughout your career. Who This Book Is For Early-career data scientists, managers of data scientists, and those interested in entering the field of data science

Data Science Projects with Python

Download Data Science Projects with Python PDF Online Free

Author :
Publisher : Packt Publishing Ltd
ISBN 13 : 1800569440
Total Pages : 433 pages
Book Rating : 4.8/5 (5 download)

DOWNLOAD NOW!


Book Synopsis Data Science Projects with Python by : Stephen Klosterman

Download or read book Data Science Projects with Python written by Stephen Klosterman and published by Packt Publishing Ltd. This book was released on 2021-07-29 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost Key FeaturesThink critically about data and use it to form and test a hypothesisChoose an appropriate machine learning model and train it on your dataCommunicate data-driven insights with confidence and clarityBook Description If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable. In this book, you'll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you'll experience in real-world data science projects. You'll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest. Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. By the end of this data science book, you'll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data. What you will learnLoad, explore, and process data using the pandas Python packageUse Matplotlib to create compelling data visualizationsImplement predictive machine learning models with scikit-learnUse lasso and ridge regression to reduce model overfittingEvaluate random forest and logistic regression model performanceDeliver business insights by presenting clear, convincing conclusionsWho this book is for Data Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you're keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.

R for Data Science

Download R for Data Science PDF Online Free

Author :
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491910364
Total Pages : 521 pages
Book Rating : 4.4/5 (919 download)

DOWNLOAD NOW!


Book Synopsis R for Data Science by : Hadley Wickham

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Executing Data Quality Projects

Download Executing Data Quality Projects PDF Online Free

Author :
Publisher : Elsevier
ISBN 13 : 0080558399
Total Pages : 353 pages
Book Rating : 4.0/5 (85 download)

DOWNLOAD NOW!


Book Synopsis Executing Data Quality Projects by : Danette McGilvray

Download or read book Executing Data Quality Projects written by Danette McGilvray and published by Elsevier. This book was released on 2008-09-01 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her "Ten Steps approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach—in which she has trained Fortune 500 clients and hundreds of workshop attendees—applies to all types of data and to all types of organizations.* Includes numerous templates, detailed examples, and practical advice for executing every step of the "Ten Steps approach.* Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices.* A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online.

Management of IOT Open Data Projects in Smart Cities

Download Management of IOT Open Data Projects in Smart Cities PDF Online Free

Author :
Publisher : Academic Press
ISBN 13 : 0128187808
Total Pages : 256 pages
Book Rating : 4.1/5 (281 download)

DOWNLOAD NOW!


Book Synopsis Management of IOT Open Data Projects in Smart Cities by : Cezary Orlowski

Download or read book Management of IOT Open Data Projects in Smart Cities written by Cezary Orlowski and published by Academic Press. This book was released on 2020-09-24 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Management of IoT Open Data Projects in Smart Cities demonstrates a key project management methodology for the implementation of Smart Cities projects: Principles and Regulations for Smart Cities (PaRSC). This methodology adopts a basis in classic Scrum soft management methods with carefully considered expansions. These include design principals for high-level architecture design and recommendations for design at the level of project teams. This approach enables the deployment of rule-based linguistic models for IoT project management, supporting the design of high-level architecture and providing rules for Scrum Smart Cities team. After reading this book, the reader will have a thorough grounding in IoT nodes and methods of their design, the acquisition and use of open data, and the use of project management methods to collect open data and build business models based on them. - Presents a unified method for smart urban interventions based on the adjustment of Scrum to the complexity of smart city projects - Establishes a key model for intelligent systems verification in Smart Cities projects - Demonstrates how practitioners can gain from the adoption of rule-based linguistic models

Leading Complex Projects

Download Leading Complex Projects PDF Online Free

Author :
Publisher : John Wiley & Sons
ISBN 13 : 111938219X
Total Pages : 261 pages
Book Rating : 4.1/5 (193 download)

DOWNLOAD NOW!


Book Synopsis Leading Complex Projects by : Edward W. Merrow

Download or read book Leading Complex Projects written by Edward W. Merrow and published by John Wiley & Sons. This book was released on 2018-05-01 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative analysis of outcomes vs PMs at the individual level Leading Complex Projects takes a unique approach to post-mortem analysis to provide project managers with invaluable insight. For the first time, individual PM characteristics are quantitatively linked to project outcomes through a major study investigating the role of project leadership in the success and failure of complex industrial projects; hard data on the backgrounds, education, and personality characteristics of over 100 directors of complex projects is analyzed against the backdrop of project performance to provide insight into controllable determinants of outcomes. By placing these analyses alongside their own data, PMs will gain greater insight into areas of weakness and strength, locate recurring obstacles, and identify project components in need of greater planning, oversight, or control. The role of leadership is to deliver results; in project management, this means taking responsibility for project outcomes. PMs are driven by continuous improvement, and this book provides a wealth of insight to help you achieve the next step forward. Understand why small, simple projects consistently outperform larger, more complex projects Delve into the project manager's role in generating successful outcomes Examine the data from over 100 PMs of complex industrial projects Link PM characteristics to project outcome to find areas for improvement Complex industrial projects from around the world provide a solid basis for quantitative analysis of outcomes—and the PMs who drive them. Although the majority of the data is taken from projects in the petroleum industry, the insights gleaned from analysis are widely applicable across industry lines for PMs who lead complex projects of any stripe. Leading Complex Projects provides clear, data-backed improvement guidance for anyone in a project management role.

Data Science Bookcamp

Download Data Science Bookcamp PDF Online Free

Author :
Publisher : Simon and Schuster
ISBN 13 : 1638352305
Total Pages : 702 pages
Book Rating : 4.6/5 (383 download)

DOWNLOAD NOW!


Book Synopsis Data Science Bookcamp by : Leonard Apeltsin

Download or read book Data Science Bookcamp written by Leonard Apeltsin and published by Simon and Schuster. This book was released on 2021-12-07 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution

Smarter Data Science

Download Smarter Data Science PDF Online Free

Author :
Publisher : John Wiley & Sons
ISBN 13 : 111969342X
Total Pages : 382 pages
Book Rating : 4.1/5 (196 download)

DOWNLOAD NOW!


Book Synopsis Smarter Data Science by : Neal Fishman

Download or read book Smarter Data Science written by Neal Fishman and published by John Wiley & Sons. This book was released on 2020-04-14 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how. Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.

Commercial Data Mining

Download Commercial Data Mining PDF Online Free

Author :
Publisher : Elsevier
ISBN 13 : 012416658X
Total Pages : 361 pages
Book Rating : 4.1/5 (241 download)

DOWNLOAD NOW!


Book Synopsis Commercial Data Mining by : David Nettleton

Download or read book Commercial Data Mining written by David Nettleton and published by Elsevier. This book was released on 2014-01-29 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling. Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book. - Illustrates cost-benefit evaluation of potential projects - Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools - Approachable reference can be read from cover to cover by readers of all experience levels - Includes practical examples and case studies as well as actionable business insights from author's own experience

Common Data Sense for Professionals

Download Common Data Sense for Professionals PDF Online Free

Author :
Publisher : CRC Press
ISBN 13 : 1000514110
Total Pages : 98 pages
Book Rating : 4.0/5 (5 download)

DOWNLOAD NOW!


Book Synopsis Common Data Sense for Professionals by : Rajesh Jugulum

Download or read book Common Data Sense for Professionals written by Rajesh Jugulum and published by CRC Press. This book was released on 2022-01-27 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data is an intrinsic part of our daily lives. Everything we do is a data point. Many of these data points are recorded with the intent to help us lead more efficient lives. We have apps that track our workouts, sleep, food intake, and personal finance. We use the data to make changes to our lives based on goals we have set for ourselves. Businesses use vast collections of data to determine strategy and marketing. Data scientists take data, analyze it, and create models to help solve problems. You may have heard of companies having data management teams or chief information officers (CIOs) or chief data officers (CDOs), etc. They are all people who work with data, but their function is more related to vetting data and preparing it for use by data scientists. The jump from personal data usage for self-betterment to mass data analysis for business process improvement often feels bigger to us than it is. In turn, we often think big data analysis requires tools held only by advanced degree holders. Although advanced degrees are certainly valuable, this book illustrates how it is not a requirement to adequately run a data science project. Because we are all already data users, with some simple strategies and exposure to basic analytical software programs, anyone who has the proper tools and determination can solve data science problems. The process presented in this book will help empower individuals to work on and solve data-related challenges. The goal of this book is to provide a step-by-step guide to the data science process so that you can feel confident in leading your own data science project. To aid with clarity and understanding, the author presents a fictional restaurant chain to use as a case study, illustrating how the various topics discussed can be applied. Essentially, this book helps traditional businesspeople solve data-related problems on their own without any hesitation or fear. The powerful methods are presented in the form of conversations, examples, and case studies. The conversational style is engaging and provides clarity.

Data Analysis for Business, Economics, and Policy

Download Data Analysis for Business, Economics, and Policy PDF Online Free

Author :
Publisher : Cambridge University Press
ISBN 13 : 1108483011
Total Pages : 741 pages
Book Rating : 4.1/5 (84 download)

DOWNLOAD NOW!


Book Synopsis Data Analysis for Business, Economics, and Policy by : Gábor Békés

Download or read book Data Analysis for Business, Economics, and Policy written by Gábor Békés and published by Cambridge University Press. This book was released on 2021-05-06 with total page 741 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.

Managing Data Science

Download Managing Data Science PDF Online Free

Author :
Publisher : Packt Publishing Ltd
ISBN 13 : 1838824561
Total Pages : 276 pages
Book Rating : 4.8/5 (388 download)

DOWNLOAD NOW!


Book Synopsis Managing Data Science by : Kirill Dubovikov

Download or read book Managing Data Science written by Kirill Dubovikov and published by Packt Publishing Ltd. This book was released on 2019-11-12 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Modern Data Science with R

Download Modern Data Science with R PDF Online Free

Author :
Publisher : CRC Press
ISBN 13 : 0429575394
Total Pages : 830 pages
Book Rating : 4.4/5 (295 download)

DOWNLOAD NOW!


Book Synopsis Modern Data Science with R by : Benjamin S. Baumer

Download or read book Modern Data Science with R written by Benjamin S. Baumer and published by CRC Press. This book was released on 2021-03-31 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.

Data Teams

Download Data Teams PDF Online Free

Author :
Publisher :
ISBN 13 : 9781484262290
Total Pages : pages
Book Rating : 4.2/5 (622 download)

DOWNLOAD NOW!


Book Synopsis Data Teams by : Jesse Anderson

Download or read book Data Teams written by Jesse Anderson and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: